• International conference on construction engineering and project management
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• 2022.06a
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• pp.353-360
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• 2022

Due to the dense and complicated working environment, the construction industry is susceptible to many accidents. Worker's fall is a severe problem at the construction site, including falling into holes or openings because of the inadequate coverings as per the safety rules. During the construction or demolition of a building, openings and holes are formed in the floors and roofs. Many workers neglect to cover openings for ease of work while being aware of the risks of holes, openings, and gaps at heights. However, there are safety rules for worker safety; the holes and openings must be covered to prevent falls. The safety inspector typically examines it by visiting the construction site, which is time-consuming and requires safety manager efforts. Therefore, this study presented a worker-driven approach (the worker is involved in the reporting process) to facilitate safety managers by developing integrated computer vision and inertia sensors-based mobile applications to identify openings. The TensorFlow framework is used to design Convolutional Neural Network (CNN); the designed CNN is trained on a custom dataset for binary class openings and covered and deployed on an android smartphone. When an application captures an image, the device also extracts the accelerometer values to determine the inclination in parallel with the classification task of the device to predict the final output as floor (openings/ covered), wall (openings/covered), and roof (openings / covered). The proposed worker-driven approach will be extended with other case scenarios at the construction site.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1026-1034
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• 2021

Tire wear and defect are important factors for safe driving condition. These defects are generally inspected by some specialized experts or very expensive equipments such as stereo depth camera and depth gauge. In this paper, we propose tire safety vision inspector based on deep neural network (DNN). The status of tire wear is categorized into three: 'safety', 'warning', and 'danger' based on depth of tire tread. We propose an attention mechanism for emphasizing the feature of tread area. The attention-based feature is concatenated to output feature maps of the last convolution layer of ResNet-101 to extract more robust feature. Through experiments, the proposed tire wear classification model improves 1.8% of accuracy compared to the existing ResNet-101 model. For detecting the tire defections, the developed tire defect detection model shows up-to 91% of accuracy using the Mask R-CNN model. From these results, we can see that the suggested models are useful for checking on the safety condition of working tire in real environment.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1130-1135
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• 2015

The bond pull test is the most widely used technique for the evaluation and control of wire bond quality. The wire being tested is pulled upward until the wire or bond to the die or substrate breaks. The inspector test strength of wire by manually and it takes around 3 minutes to perform the test. In this paper, we develop a 3D vision system to measure 3D position of wire. It gives 3D position data of wire to move a hook into wires. The 3D measurement method to use here is a confocal imaging system. The conventional confocal imaging system is a spot scanning method which has a high resolution and good illumination efficiency. However, a conventional confocal systems has a disadvantage to perform XY axis scanning in order to achieve 3D data in given FOV (Field of View) through spot scanning. We propose a method to improve a parallel mode confocal system using a micro-lens and pin-hole array to remove XY scan. 2D imaging system can detect 2D location of wire and it can reduce time to measure 3D position of wire. In the experimental results, the proposed system can measure 3D position of wire with reasonable accuracy.